Second-order Non-local Attention Networks for Person Re-identification

Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts. Some efforts use soft attention to reallocate distant outliers to their most similar parts, while others adjust part granularity to incorporate more distant positions for learning the relationships. Others seek to generalize part-based methods by introducing a dropout mechanism on consecutive regions of the feature map to enhance distant region relationships. In this paper, we propose a novel attention mechanism to directly model long-range relationships via second-order feature statistics. When combined with a generalized DropBlock module, our method performs equally to or better than state-of-the-art results for mainstream person re-identification datasets, including Market1501, CUHK03, and DukeMTMC-reID. 1. Introduction Person re-identification (re-ID) is an essential component of intelligent surveillance systems, which draws increasing interest from the computer vision community. It is challenging to associate multiple images captured by cameras with non-overlapping viewpoints with the same person-of-interest. Specifically, this task is challenging due to the dramatic variations with respect to illumination, occlusion, resolution, human pose, view angle, clothing, and background. The person re-ID research community has proposed various effective handcrafted features [2, 20, 26, 28, 24, 6, 21, 25] to address these challenges. Methods based on deep convolutional networks have also been introduced to learn discriminative features and representations that are robust to these variations, thereby pushing multiple re-ID benchmarks to a whole new level.